# QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks

arXiv: https://arxiv.org/abs/2605.24218 · HTML: https://mlsanity.com/paper/2605.24218/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 22 May 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 26 of 26)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [Kimi K2.5: Visual Agentic Intelligence](https://mlsanity.com/paper/2602.02276/) (2026)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [WebSailor: Navigating Super-human Reasoning for Web Agent](https://mlsanity.com/paper/2507.02592/) (2025)
- [BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent](https://mlsanity.com/paper/2508.06600/) (2025)
- [WebDancer: Towards Autonomous Information Seeking Agency](https://mlsanity.com/paper/2505.22648/) (2025)
- [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https://mlsanity.com/paper/2509.13313/) (2025)
- [AI-Researcher: Autonomous Scientific Innovation](https://mlsanity.com/paper/2505.18705/) (2025)
- [Agent Learning via Early Experience](https://mlsanity.com/paper/2510.08558/) (2025)
- [ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents](https://mlsanity.com/paper/2511.07685/) (2025)
- [Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge](https://mlsanity.com/paper/2506.21506/) (2025)
- [Tongyi DeepResearch Technical Report](https://mlsanity.com/paper/2510.24701/) (2025)
- [REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents](https://mlsanity.com/paper/2602.14234/) (2026)
- [LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild](https://mlsanity.com/paper/2510.14240/) (2025)
- [FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning](https://mlsanity.com/paper/2509.13160/) (2025)

## Built on by (most cited first, 11 of 11)

- [AREX: Towards a Recursively Self-Improving Agent for Deep Research](https://mlsanity.com/paper/2607.21461/) (2026)
- [Scaling Automatic Research Agents via World Models](https://mlsanity.com/paper/2608.12564/) (2026)
- [Deep Research Pretraining via Predictive Navigation](https://mlsanity.com/paper/2608.00432/) (2026)
- [ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment](https://mlsanity.com/paper/2608.05102/) (2026)
- [SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents](https://mlsanity.com/paper/2608.05212/) (2026)
- [Mint-Agent: Introducing Finance-Native Agentic Foundation Models](https://mlsanity.com/paper/2608.16386/) (2026)
- [Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents](https://mlsanity.com/paper/2609.34296/) (2026)
- [BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning](https://mlsanity.com/paper/2609.36505/) (2026)
- [Programmatic Search Agents: Extending Agentic Search Beyond Query Reformulation](https://mlsanity.com/paper/2610.06689/) (2026)
- [Avoiding What Your Teacher Already Knows: Entity Familiarity for Deep-Search Distillation](https://mlsanity.com/paper/2610.16251/) (2026)
- [HyWe: Unifying Search and Operation for Real-World Hybrid Web Tasks](https://mlsanity.com/paper/2610.16319/) (2026)

## Abstract (arXiv)

Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information. However, frontier systems remain proprietary, while existing open agents often generalize poorly across different task types, leaving unclear how to train a broadly capable deep research agent. We release QUEST, a family of open models (ranging from 2B to 35B) that serve as general-purpose deep research agents designed to handle a wide range of long-horizon search tasks, with strong capabilities in fact seeking, citation grounding, and report synthesis. To build QUEST, we propose an effective training recipe combining mid-training, supervised fine-tuning, and reinforcement learning. Central to this recipe is a curated data synthesis pipeline based on unified rubric trees, which applies to different task types and enables synthesizing training data with verifiable rewards without human annotation. In addition, QUEST incorporates a built-in context management mechanism that enables effective long-horizon reasoning and knowledge synthesis. Using only 8K synthesized tasks, QUEST approaches or even surpasses frontier closed-source agents across eight deep research benchmarks spanning diverse task types, and achieves the best overall performance among recent open-weight agents. We released everything: models, data, and training scripts.